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<li><a class="reference internal" href="#">Early stopping of Gradient Boosting</a><ul>
<li><a class="reference internal" href="#compare-scores-with-and-without-early-stopping">Compare scores with and without early stopping</a></li>
<li><a class="reference internal" href="#compare-fit-times-with-and-without-early-stopping">Compare fit times with and without early stopping</a></li>
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  <div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-ensemble-plot-gradient-boosting-early-stopping-py"><span class="std std-ref">here</span></a> to download the full example code or to run this example in your browser via Binder</p>
</div>
<div class="sphx-glr-example-title section" id="early-stopping-of-gradient-boosting">
<span id="sphx-glr-auto-examples-ensemble-plot-gradient-boosting-early-stopping-py"></span><h1>Early stopping of Gradient Boosting<a class="headerlink" href="#early-stopping-of-gradient-boosting" title="Permalink to this headline">¶</a></h1>
<p>Gradient boosting is an ensembling technique where several weak learners
(regression trees) are combined to yield a powerful single model, in an
iterative fashion.</p>
<p>Early stopping support in Gradient Boosting enables us to find the least number
of iterations which is sufficient to build a model that generalizes well to
unseen data.</p>
<p>The concept of early stopping is simple. We specify a <code class="docutils literal notranslate"><span class="pre">validation_fraction</span></code>
which denotes the fraction of the whole dataset that will be kept aside from
training to assess the validation loss of the model. The gradient boosting
model is trained using the training set and evaluated using the validation set.
When each additional stage of regression tree is added, the validation set is
used to score the model.  This is continued until the scores of the model in
the last <code class="docutils literal notranslate"><span class="pre">n_iter_no_change</span></code> stages do not improve by atleast <code class="docutils literal notranslate"><span class="pre">tol</span></code>. After
that the model is considered to have converged and further addition of stages
is “stopped early”.</p>
<p>The number of stages of the final model is available at the attribute
<code class="docutils literal notranslate"><span class="pre">n_estimators_</span></code>.</p>
<p>This example illustrates how the early stopping can used in the
<a class="reference internal" href="../../modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble.GradientBoostingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.GradientBoostingClassifier</span></code></a> model to achieve
almost the same accuracy as compared to a model built without early stopping
using many fewer estimators. This can significantly reduce training time,
memory usage and prediction latency.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Vighnesh Birodkar &lt;vighneshbirodkar@nyu.edu&gt;</span>
<span class="c1">#          Raghav RV &lt;rvraghav93@gmail.com&gt;</span>
<span class="c1"># License: BSD 3 clause</span>

<span class="kn">import</span> <span class="nn">time</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>

<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">ensemble</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>

<span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>

<span class="n">data_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span><span class="p">(),</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">()]</span>
<span class="n">data_list</span> <span class="o">=</span> <span class="p">[(</span><span class="n">d</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">d</span><span class="o">.</span><span class="n">target</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data_list</span><span class="p">]</span>
<span class="n">data_list</span> <span class="o">+=</span> <span class="p">[</span><span class="n">datasets</span><span class="o">.</span><span class="n">make_hastie_10_2</span><span class="p">()]</span>
<span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Iris Data&#39;</span><span class="p">,</span> <span class="s1">&#39;Digits Data&#39;</span><span class="p">,</span> <span class="s1">&#39;Hastie Data&#39;</span><span class="p">]</span>

<span class="n">n_gb</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">score_gb</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">time_gb</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">n_gbes</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">score_gbes</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">time_gbes</span> <span class="o">=</span> <span class="p">[]</span>

<span class="n">n_estimators</span> <span class="o">=</span> <span class="mi">500</span>

<span class="k">for</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">data_list</span><span class="p">:</span>
    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
                                                        <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

    <span class="c1"># We specify that if the scores don&#39;t improve by atleast 0.01 for the last</span>
    <span class="c1"># 10 stages, stop fitting additional stages</span>
    <span class="n">gbes</span> <span class="o">=</span> <span class="n">ensemble</span><span class="o">.</span><span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="n">n_estimators</span><span class="p">,</span>
                                               <span class="n">validation_fraction</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
                                               <span class="n">n_iter_no_change</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
                                               <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">gb</span> <span class="o">=</span> <span class="n">ensemble</span><span class="o">.</span><span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="n">n_estimators</span><span class="p">,</span>
                                             <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
    <span class="n">gb</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
    <span class="n">time_gb</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span><span class="p">)</span>

    <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
    <span class="n">gbes</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
    <span class="n">time_gbes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span><span class="p">)</span>

    <span class="n">score_gb</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">gb</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span>
    <span class="n">score_gbes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">gbes</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span>

    <span class="n">n_gb</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">gb</span><span class="o">.</span><span class="n">n_estimators_</span><span class="p">)</span>
    <span class="n">n_gbes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">gbes</span><span class="o">.</span><span class="n">n_estimators_</span><span class="p">)</span>

<span class="n">bar_width</span> <span class="o">=</span> <span class="mf">0.2</span>
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data_list</span><span class="p">)</span>
<span class="n">index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span> <span class="o">*</span> <span class="n">bar_width</span><span class="p">,</span> <span class="n">bar_width</span><span class="p">)</span> <span class="o">*</span> <span class="mf">2.5</span>
<span class="n">index</span> <span class="o">=</span> <span class="n">index</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">n</span><span class="p">]</span>
</pre></div>
</div>
<div class="section" id="compare-scores-with-and-without-early-stopping">
<h2>Compare scores with and without early stopping<a class="headerlink" href="#compare-scores-with-and-without-early-stopping" title="Permalink to this headline">¶</a></h2>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>

<span class="n">bar1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">score_gb</span><span class="p">,</span> <span class="n">bar_width</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Without early stopping&#39;</span><span class="p">,</span>
               <span class="n">color</span><span class="o">=</span><span class="s1">&#39;crimson&#39;</span><span class="p">)</span>
<span class="n">bar2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">index</span> <span class="o">+</span> <span class="n">bar_width</span><span class="p">,</span> <span class="n">score_gbes</span><span class="p">,</span> <span class="n">bar_width</span><span class="p">,</span>
               <span class="n">label</span><span class="o">=</span><span class="s1">&#39;With early stopping&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;coral&#39;</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">index</span> <span class="o">+</span> <span class="n">bar_width</span><span class="p">,</span> <span class="n">names</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">))</span>


<span class="k">def</span> <span class="nf">autolabel</span><span class="p">(</span><span class="n">rects</span><span class="p">,</span> <span class="n">n_estimators</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Attach a text label above each bar displaying n_estimators of each model</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">rect</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">rects</span><span class="p">):</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">rect</span><span class="o">.</span><span class="n">get_x</span><span class="p">()</span> <span class="o">+</span> <span class="n">rect</span><span class="o">.</span><span class="n">get_width</span><span class="p">()</span> <span class="o">/</span> <span class="mf">2.</span><span class="p">,</span>
                 <span class="mf">1.05</span> <span class="o">*</span> <span class="n">rect</span><span class="o">.</span><span class="n">get_height</span><span class="p">(),</span> <span class="s1">&#39;n_est=</span><span class="si">%d</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">n_estimators</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
                 <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>


<span class="n">autolabel</span><span class="p">(</span><span class="n">bar1</span><span class="p">,</span> <span class="n">n_gb</span><span class="p">)</span>
<span class="n">autolabel</span><span class="p">(</span><span class="n">bar2</span><span class="p">,</span> <span class="n">n_gbes</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Datasets&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Test score&#39;</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="compare-fit-times-with-and-without-early-stopping">
<h2>Compare fit times with and without early stopping<a class="headerlink" href="#compare-fit-times-with-and-without-early-stopping" title="Permalink to this headline">¶</a></h2>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>

<span class="n">bar1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">time_gb</span><span class="p">,</span> <span class="n">bar_width</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Without early stopping&#39;</span><span class="p">,</span>
               <span class="n">color</span><span class="o">=</span><span class="s1">&#39;crimson&#39;</span><span class="p">)</span>
<span class="n">bar2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">index</span> <span class="o">+</span> <span class="n">bar_width</span><span class="p">,</span> <span class="n">time_gbes</span><span class="p">,</span> <span class="n">bar_width</span><span class="p">,</span>
               <span class="n">label</span><span class="o">=</span><span class="s1">&#39;With early stopping&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;coral&#39;</span><span class="p">)</span>

<span class="n">max_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">amax</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">time_gb</span><span class="p">,</span> <span class="n">time_gbes</span><span class="p">))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">index</span> <span class="o">+</span> <span class="n">bar_width</span><span class="p">,</span> <span class="n">names</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.3</span> <span class="o">*</span> <span class="n">max_y</span><span class="p">,</span> <span class="mi">13</span><span class="p">))</span>

<span class="n">autolabel</span><span class="p">(</span><span class="n">bar1</span><span class="p">,</span> <span class="n">n_gb</span><span class="p">)</span>
<span class="n">autolabel</span><span class="p">(</span><span class="n">bar2</span><span class="p">,</span> <span class="n">n_gbes</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.3</span> <span class="o">*</span> <span class="n">max_y</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Datasets&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Fit Time&#39;</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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